End of training

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,566 @@
+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:477792
+- loss:CachedMultipleNegativesRankingLoss
+base_model: answerdotai/ModernBERT-base
+widget:
+- source_sentence: tachyphylaxis definition
+  sentences:
+  - 1 In some areas plumbers charge $45 -$75 an hour; in other regions the hourly
+    rate can be $75 -$150. 2  Most plumbers charge a two-hour minimum or a service
+    call fee of $75 -$150, and some plumbers bill a flat fee per job instead of an
+    hourly rate.3  Either away, exact costs will depend on the complexity and type
+    of work done. Plumbers' rates vary significantly by location. 2  In some areas
+    plumbers charge $45 -$75 an hour; in other regions the hourly rate can be $75
+    -$150.
+  - "Medical Definition of tachyphylaxis. plural. tachyphylaxes. \\-Ë\x8CsÄ\x93z\\\
+    play. : diminished response to later increments in a sequence of applications\
+    \ of a physiologically active substance (as the diminished pressor response that\
+    \ follows repeated injections of renin)"
+  - Quick Answer. Injury to the phrenic nerve can paralyze the diaphragm and have
+    a serious impact on the regulation of breathing, such as difficulty during inhalation,
+    according to the UCLA Division of Plastic & Reconstructive Surgery. The phrenic
+    nerve is responsible for the function of the diaphragm. Continue Reading.
+- source_sentence: where is st malo beach
+  sentences:
+  - Nausea is a sensation of discomfort in the upper abdomen, accompanied by an urge
+    to vomit. Also known of as qualm, nausea may be a side effect associated with
+    several medications or a symptom of disease or disorder. Sometimes large, fatty
+    or sugary meals may also lead to a feeling of nausea. Nausea is a sensation of
+    discomfort in the upper abdomen, accompanied by an urge to vomit. Also known of
+    as qualm, nausea may be a side effect associated with several medications or a
+    symptom of disease or disorder.
+  - Location of Pennsylvania in the United States. Folsom is a census-designated place
+    (CDP) in Delaware County, Pennsylvania, United States. It is part of Ridley Township.
+    The population was 8,323 at the 2010 census.
+  - "Saint Malo Beach Oceanside homes. Developed in the late 1920â\x80\x99s, the community\
+    \ of Saint Malo Beach is one of the highlights of beautiful Carlsbad, CA â\x80\
+    \x93 and one of its most secluded hideaways."
+- source_sentence: who invented cotton candy dr pepper and electric chair
+  sentences:
+  - "Skill positions in football are the positions that are most responsible for causing\
+    \ or preventing points from being scored. The skill positions are: Skill positions\
+    \ are often contrasted with linemen â\x80\x93 players who line up along the line\
+    \ of scrimmage. Skill position players are generally smaller than linemen, but\
+    \ they must also be faster and have other talents (such as the ability to throw\
+    \ or catch the ball, cover an opposing receiver, or to dodge opponents) that rely\
+    \ more on finesse than on brute force."
+  - A WBS Dictionary is merely a supporting document, which provides the definitions
+    for each component contained in the Work Breakdown Structure. This type of dictionary
+    is often recommended as a reference resource material for task-oriented projects
+    comprising several work phases.
+  - 'Cotton Candy (1897): Cotton Candy was invented in 1897 by the American inventors
+    William Morrison and John C. Wharton. Cotton Gin (1793): The Cotton Gin was invented
+    in 1793 by the American inventor Eli Whitney during the Industrial Revolution.'
+- source_sentence: what county is boston, ma
+  sentences:
+  - 'There are two kinds of clauses: independent and dependent clauses. Most simply,
+    an independent clause can form a complete sentence on its own and a dependent
+    clause cannot (at least, not by itself). Think of it this way: an independent
+    clause is like a cup of coffee, and a dependent clause is like a caffeine lover.
+    Caffeine lovers are dependent on coffee, so the two can be joined (quite happily)
+    to form a cohesive unit. Similarly, two cups of coffee, or two independent clauses,
+    can be combined.'
+  - Boston is in the county of Suffolk in Massachusetts. The population is about 722,023,
+    and Boston is the largest city.
+  - 'Pre-diabetes is diagnosed by any one of the following: 1  A fasting blood glucose
+    in between 100-125 mg/dL. 2  An A1c between 5.7 - 6.4 percent.  Any value between
+    140 mg/dL and 199 mg/dL during a two-hour 75g oral glucose tolerance test.'
+- source_sentence: product key windows 8.1 how to find
+  sentences:
+  - 'If Windows 8.1 came preinstalled on your computer, your Windows 8.1 product key
+    should be on a sticker on your computer or with your documentation. The Windows
+    8.1 product key is a series of 25 letters and numbers and should look like this:
+    xxxxx-xxxxx-xxxxx-xxxxx-xxxxx.'
+  - Al Gore not divorced from wife Tipper, confirms relationship with longtime girlfriend.
+    1  Pucker up! Al Gore planted a wet one on wife Tipper in 2000, during his presidential
+    campaign. Ten years later the couple separated after 40 years of marriage.
+  - springer spaniel. n. 1. (Breeds) either of two breeds of large quick-moving spaniels
+    bred to spring game, having a slightly domed head and ears of medium length. The
+    English springer spaniel is the larger and can be of various colours; the Welsh
+    springer spaniel is always a rich red and white. n.
+datasets:
+- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on answerdotai/ModernBERT-base
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: eval
+      type: eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7949656022587187
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9252395912037221
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9530759136278681
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9735157275221696
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7949656022587187
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3084131970679073
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19061518272557365
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09735157275221698
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7949656022587187
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9252395912037221
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9530759136278681
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9735157275221696
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8908733180956838
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8636181159543801
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.864765622765834
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on answerdotai/ModernBERT-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'OptimizedModule'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("modernbert-msmarco")
+# Run inference
+queries = [
+    "product key windows 8.1 how to find",
+]
+documents = [
+    'If Windows 8.1 came preinstalled on your computer, your Windows 8.1 product key should be on a sticker on your computer or with your documentation. The Windows 8.1 product key is a series of 25 letters and numbers and should look like this: xxxxx-xxxxx-xxxxx-xxxxx-xxxxx.',
+    'springer spaniel. n. 1. (Breeds) either of two breeds of large quick-moving spaniels bred to spring game, having a slightly domed head and ears of medium length. The English springer spaniel is the larger and can be of various colours; the Welsh springer spaniel is always a rich red and white. n.',
+    'Al Gore not divorced from wife Tipper, confirms relationship with longtime girlfriend. 1  Pucker up! Al Gore planted a wet one on wife Tipper in 2000, during his presidential campaign. Ten years later the couple separated after 40 years of marriage.',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 768] [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.8319, -0.0147, -0.0184]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `eval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.795      |
+| cosine_accuracy@3   | 0.9252     |
+| cosine_accuracy@5   | 0.9531     |
+| cosine_accuracy@10  | 0.9735     |
+| cosine_precision@1  | 0.795      |
+| cosine_precision@3  | 0.3084     |
+| cosine_precision@5  | 0.1906     |
+| cosine_precision@10 | 0.0974     |
+| cosine_recall@1     | 0.795      |
+| cosine_recall@3     | 0.9252     |
+| cosine_recall@5     | 0.9531     |
+| cosine_recall@10    | 0.9735     |
+| **cosine_ndcg@10**  | **0.8909** |
+| cosine_mrr@10       | 0.8636     |
+| cosine_map@100      | 0.8648     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
+
+* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
+* Size: 477,792 training samples
+* Columns: <code>query</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                            | positive                                                                            |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 81.72 tokens</li><li>max: 205 tokens</li></ul> |
+* Samples:
+  | query                                                      | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:-----------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>what is the farthest distance in the universe</code> | <code>Depends on what you mean by seeing. The particle horizon is just the furthest. distance light could have traveled to us since the universe began. That is 93 billion. light years in diameter, or 47 billion light years in any direction, but we can't actually. see anything at that distance.</code>                                                                                                                                                                                                                                                                                              |
+  | <code>what county is laurel ms  in</code>                  | <code>Laurel, MS. Online Offers. Laurel is a city located in Jones County in Mississippi, a state of the United States of America. As of the 2000 census, the city had a total population of 18,393 although a significant population increase has been reported following Hurricane Katrina. Located in southeast Mississippi, southeast of Jackson on Tallahala Creek, Laurel was founded in 1882 as a lumber town. An American Indian reservation is located in nearby Sandersville. Laurel is the principal city of the Laurel Micropolitan Statistical Area.</code>                                   |
+  | <code>how to use a beadloom</code>                         | <code>How to string your bead loom. To string a loom, attach your nymo thread to one of the small nails at the end of the loom. Run the thread over the metal bars (located on both ends of the loom) and wrap it around one of the small nails on the other end of your loom.ize 8 seed beads are normally to heavy to be used on a loom. The end result would be beadwork that sags in the middle. Every other slow on the metal bar was skipped to accomodate size 8 seed beads. You will not need to do this with seed beads sizes 10-15 that are the correct size beads to use on a bead loom.</code> |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 64,
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Evaluation Dataset
+
+#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
+
+* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
+* Size: 25,147 evaluation samples
+* Columns: <code>query</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                            | positive                                                                            |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.24 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 81.64 tokens</li><li>max: 198 tokens</li></ul> |
+* Samples:
+  | query                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:--------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>how long it take for a pimple to burst</code>                       | <code>Start doing warm compress over the pimple, so that it gradually gets drained over 2 to 3 days. Do it gently, without giving yourself much pain. Also, apply mupirocin ointment over it twice a day for 3 to 4 days.Read above in detail about dealing with your infected pimple.tart doing warm compress over the pimple, so that it gradually gets drained over 2 to 3 days. Do it gently, without giving yourself much pain. Also, apply mupirocin ointment over it twice a day for 3 to 4 days.</code> |
+  | <code>tularosa population</code>                                          | <code>The Village of Tularosa had a population of 2,677 as of July 1, 2017. Tularosa ranks in the upper quartile for Population Density when compared to the other cities, towns and Census Designated Places (CDPs) in New Mexico. See peer rankings below. The primary coordinate point for Tularosa is located at latitude 33.075 and longitude -106.0173 in Otero County.</code>                                                                                                                            |
+  | <code>do some people have their blood flowing in reverse direction</code> | <code>As a result, not enough blood flows through the valve. Some valves can have both stenosis and backflow problems. Atresia occurs if a heart valve lacks an opening for blood to pass through. Some people are born with heart valve disease, while others acquire it later in life.</code>                                                                                                                                                                                                                 |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 64,
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 1024
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `warmup_steps`: 0.1
+- `bf16`: True
+- `eval_strategy`: epoch
+- `per_device_eval_batch_size`: 1024
+- `push_to_hub`: True
+- `hub_model_id`: modernbert-msmarco
+- `load_best_model_at_end`: True
+- `dataloader_num_workers`: 4
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 1024
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0.1
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 1
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: True
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `eval_strategy`: epoch
+- `per_device_eval_batch_size`: 1024
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: True
+- `hub_private_repo`: None
+- `hub_model_id`: modernbert-msmarco
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 4
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch   | Step    | Training Loss | Validation Loss | eval_cosine_ndcg@10 |
+|:-------:|:-------:|:-------------:|:---------------:|:-------------------:|
+| 0.1071  | 50      | 4.1149        | -               | -                   |
+| 0.2141  | 100     | 0.5296        | -               | -                   |
+| 0.3212  | 150     | 0.3000        | -               | -                   |
+| 0.4283  | 200     | 0.2463        | -               | -                   |
+| 0.5353  | 250     | 0.2247        | -               | -                   |
+| 0.6424  | 300     | 0.2032        | -               | -                   |
+| 0.7495  | 350     | 0.1923        | -               | -                   |
+| 0.8565  | 400     | 0.1900        | -               | -                   |
+| 0.9636  | 450     | 0.1888        | -               | -                   |
+| **1.0** | **467** | **-**         | **0.1889**      | **0.8768**          |
+| 0.1071  | 50      | 0.1866        | -               | -                   |
+| 0.2141  | 100     | 0.1560        | -               | -                   |
+| 0.3212  | 150     | 0.1455        | -               | -                   |
+| 0.4283  | 200     | 0.1377        | -               | -                   |
+| 0.5353  | 250     | 0.1397        | -               | -                   |
+| 0.6424  | 300     | 0.1351        | -               | -                   |
+| 0.7495  | 350     | 0.1355        | -               | -                   |
+| 0.8565  | 400     | 0.1417        | -               | -                   |
+| 0.9636  | 450     | 0.1468        | -               | -                   |
+| **1.0** | **467** | **-**         | **0.1512**      | **0.8909**          |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.3.0
+- Transformers: 5.3.0
+- PyTorch: 2.10.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 4.7.0
+- Tokenizers: 0.22.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "5.3.0",
+    "pytorch": "2.10.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}